Reduce Labor Costs Research Articles

Optimal Artificial Neural Network-based Fabric Defect Detection and Classification

Automated Fabric Defect (FD) detection plays a crucial role in industrial automation within fabric production. Traditionally, the identification of FDs heavily relies on manual assessment, facilitating prompt repairs of minor defects. However, the efficiency of manual recognition diminishes significantly as labor working hours increase. Consequently, there is a pressing need to introduce an automated analysis method for FD recognition to reduce labor costs, minimize errors, and improve fabric quality. Many researchers have devised defect detection systems utilizing Machine Learning (ML) approaches, enabling swift, accurate, and efficient identification of defects. This study presents the Optimal Artificial Neural Network-based Fabric Defect Detection and Classification (OANN-FDDC) technique. The OANN-FDDC technique exploits handcrafted features with a parameter-tuning strategy for effectively detecting the FD process. To obtain this, the OANN-FDDC technique employs CLAHE and Bilateral Filtering (BF) model-based contrast augmentation and noise removal. Besides, the OANN-FDDC technique extracts shape, texture, and color features. For FD detection, the ANN method is utilized. To improve the detection results of the ANN method, the Root Mean Square Propagation (RMSProp) optimization technique is used for the parameter selection process. The simulation outputs of the OANN-FDDC technique were examined on an open fabric image database. The experimental results of the OANN-FDDC technique implied a better outcome than the 96.97% of other recent approaches.

Comparison Study Between Weight Training and Fartlek To Improve The Running Speed Of Trainee Teachers In The 100-Meter Event

The purpose of this study is to compare the effectiveness of weight training and Fartlek in improving the running speed of trainee teachers in the 100-meter event. The study aims to determine which training method is more effective in enhancing running speed. The study participants were randomly assigned to either weight training or fartlek groups and underwent their respective training programs for four weeks. The running speed of both groups was evaluated and compared using a standardized assessment tool. The results of this study will contribute to the existing knowledge on the benefits and limitations of weight training and Fartlek in improving running speed. The t-test showed that there is no significant difference between the two means among both training groups and its proven that both weight training and Fartlek training is suitable is improving the running speed of trainee teachers in 100-meter event. This study is expected to provide valuable information for coaches, trainers, and physical educators in developing effective training programs for athletes and physical education students. and reduce labor costs and water wastage and increase productivity and efficiency.

Automatic Counting Machine with IR-Sensor Based Counting Machine in Sorting Areas Using Conveyor Belt

Absolutely, the transition from manual counting to automated counting with the help of technology has been a significant advancement across various industries. This shift has brought about increased accuracy, efficiency, and convenience in monitoring and managing processes. The use of sensors, cameras, and other smart technologies has revolutionized how businesses handle tasks that require counting or tracking. In the context of factories and stores, automated counting systems offer several advantages. They eliminate the errors associated with manual counting, reduce labor costs, and provide real-time data that can be crucial for decision-making. These systems are not only faster but also more reliable, allowing businesses to streamline their operations and improve overall productivity. Moreover, the integration of these automated counting systems with digital displays or screens makes the information easily accessible to relevant personnel. This real-time visibility enhances monitoring and control, enabling quick responses to changes or issues in the production or sales processes. The use of such technology reflects the ongoing trend of leveraging automation to enhance various aspects of work, making processes more efficient and accurate. It's indeed a testament to how innovation has transformed everyday tasks and workflows, bringing about positive changes in different industries. Key Word: Arduino Uno, I2C module, IR sensor, LCD, Buzzer, Motor, Conveyor Belt.

Analysis of practical experience in the development and implementation of standard operating procedures in the activities of nursing medical staff on the base of the FSBHI MRC «Sergievsk mineral waters» FMBA of Russia

The use of innovative technologies in the daily practice of a nurse makes her work more professional, ensures safety and convenience when performing basic professional tasks, reduces labor costs, and allows for the implementation of the diagnostic and treatment process quickly and efficiently. Target. To analyze the experience of developing and implementing standard operating procedures for nurses for 2022–2023. Results. The successful development and further implementation of SOPs for various types of activities of nursing staff was completed; solved problems that arose during the development and implementation of SOPs by creating corrective actions with mutual analysis, taking into account the feedback of the team of specialists; the proposed technologies and standard documents can be used as a model by most medical organizations. Conclusion. In the Federal State Budgetary Healthcare Institution MRC «Sergievsk Mineral Waters» FMBA of Russia, as of January 2024, the following have been developed: 121 SOPs, 30 SOPs have been revised. In 2024, 4 SOPs were introduced in medical organizations and continuous work is underway to further develop them.

Fibre reinforced concrete under in-plane shear stresses

Steel fibres have a positive influence on the shear behaviour of concrete elements, such as beams and walls, the more so as the developments in fibre reinforced concrete (FRC) technology allow reaching increasingly higher residual post-cracking strengths. The partial, or total, replacement of shear reinforcement by steel fibres allows increasing the construction speed and reducing labour costs, potentially offering economic benefits in the context of industrialized fabrication. However, the combined behaviour of steel fibres with reinforcement in shear critical elements is still not fully understood hindering the development of consistent design procedures.In this study, a detailed mechanical model based on the Cracked Membrane Model using fixed and interlocked cracks (F-CMM) is presented, which is capable of describing the behaviour up to failure of concrete panels reinforced with both steel fibres and rebars (R-FRC) subjected to in-plane shear and axial forces. The model explicitly considers all the individual mechanical phenomena governing shear behaviour of R-FRC and can serve as the basis for the development of methodologies for shear safety verifications.The model is validated against the existing experimental results from R-FRC panels subjected to in-plane shear loading. A calculation example is used to discuss in detail the model outcomes and the impact of the spatial variability in the fibre distribution. A parametric study is developed to understand the effects of fibres on the shear strength and failure modes, and its interactions with both the reinforcements and crack shear stress transfer mechanics.

Drone-mounted remote-controlled arm for monitoring and precision spraying coconut rhinoceros beetle infestations

This paper presents a low-cost (locally-assembled) drone system with a sprayer arm and a remote control for monitoring and precisely applying pesticides to coconut trees infested by the Rhinoceros Beetle. A modified RGB IP camera continuously monitors potential coconut beetle infestation points, capturing real-time spray images, and displaying the results through a smartphone interface. The system provides real-time monitoring, allowing immediate remote control of the spray operation from the ground upon detecting beetle infestations. The drone-mounted sprayer arm is controlled in response to the ground-based signals, to accurately extend or contract while effectively dispensing spray chemicals onto the target areas at the upper canopy of coconut trees. In our tests using Nam-Hom coconut trees, which averaged a height of 10.4 m, the system operated at a speed of 1.25 km/h, achieving a spraying flow rate of 0.333 l/h and a working capacity of 0.352 ha/h. The controlled spraying setup emits a solid jet, ensuring precision and effective atomization of chemicals within a distance range of 1.0 to 4.0 m from the target. The spray quality attributes (droplet size, pattern, and coverage) were not significantly affected by the wind speed. The system could potentially reduce labor costs, chemical usage, and health risks associated with pesticide application.

Genetic study for seed germination and shattering in Euphorbia lagascae in response to different seed treatments

Euphorbia lagascae Spreng is a promising emerging oilseed crop, with its seed oil accounting for approximately 50% of the seed weight. Euphorbia oil contains a significant amount of vernolic acid, comprising two-thirds of its composition, which boasts various industrial applications, including acting as a stabilizer-plasticizer and natural dye. However, this species was known to have a high degree of seed-shattering and a low germination rate, which act as two important barriers to large-scale production and exploitation. Therefore, the objective of this study is to determine the genetic control of seed germination and seed-shattering traits in order to develop a reliable pipeline that would be applicable for industries and breeders to select superior E. lagascae lines and design a robust breeding scheme in a short time at reduced labor costs. For this objective, five different wild-type genotypes of E. lagascae that demonstrated high germination potential were crossed with an ethyl methanesulfonate (EMS) mutant genotype that produces non-shattering capsules. The F2 populations from two successful crosses (A and B) were separated into three different treated groups for seed germination evaluation and to study the segregation of 200 individuals per F2 population. The three treatments were: light, gibberellic acid (GA3), and control treatment. Consequently, plants treated with approximately 250 μmol/m2/s of light showed significant improvement in germination up to 75% in cross A and 82.4 % in cross B compared with the control plants and the group treated with 0.05% GA3. According to the chi-square test results, the inheritance pattern of seed germination in response to light treatment follows a 3:1 segregation ratio between germinated and non-germinated seeds, indicating a dominant gene action in the F2 generation. The same conclusion was followed for the shattering trait in the group treated with light, which was also simply inherited as a 3:1 ratio for shattering vs. non-shattering capsules. Our results emphasize the importance and significance of light treatment in producing uniform populations through acceptable germination and shattering resistance of the mutant genotypes of E. lagascae. This is the first report of light treatment that significantly improved seed germination of E. lagascae, which may enhance efforts in the development of this new industrial crop as a feedstock for vernolic acid production.

Impact of Drip Irrigation in Sugarcane Cultivation in Mandya District of Karnataka, India

This study investigates the impact of drip irrigation on sugarcane cultivation in Mandya district, Karnataka, considering the challenges posed by traditional flood irrigation methods and the increasing demand for water resources. The research compares the adoption of drip and flood irrigation, analyzing socio-economic characteristics, cropping patterns, and the comparative economics of sugarcane cultivation under both methods. Externality concept and Partial budgeting technique was emphasized to estimate the additional benefit derived from drip irrigation over flood irrigation. The study reveals that drip irrigation leads to higher water use efficiency, improved crop quality, and environmental sustainability. The adoption of drip irrigation in sugarcane cultivation results in significant economic benefits, with higher net returns and increased yields then farmers following flood irrigation. The findings highlight the positive externality associated with drip irrigation, contributing to water conservation, reduced labor costs, and increased agricultural sustainability with an additional benefit of around Rs. 41,000 derived due to drip adoption over flood irrigation in Sugarcane cultivation. As Karnataka faces evolving challenges in agriculture, the widespread adoption of drip irrigation emerges as a pivotal factor in ensuring the prosperity of sugarcane cultivation and addressing environmental concerns.

An Apple Detection and Localization Method for Automated Harvesting under Adverse Light Conditions

The use of automation technology in agriculture has become particularly important as global agriculture is challenged by labor shortages and efficiency gains. The automated process for harvesting apples, an important agricultural product, relies on efficient and accurate detection and localization technology to ensure the quality and quantity of production. Adverse lighting conditions can significantly reduce the accuracy of fruit detection and localization in automated apple harvesting. Based on deep-learning techniques, this study aims to develop an accurate fruit detection and localization method under adverse light conditions. This paper explores the LE-YOLO model for accurate and robust apple detection and localization. The traditional YOLOv5 network was enhanced by adding an image enhancement module and an attention mechanism. Additionally, the loss function was improved to enhance detection performance. Secondly, the enhanced network was integrated with a binocular camera to achieve precise apple localization even under adverse lighting conditions. This was accomplished by calculating the 3D coordinates of feature points using the binocular localization principle. Finally, detection and localization experiments were conducted on the established dataset of apples under adverse lighting conditions. The experimental results indicate that LE-YOLO achieves higher accuracy in detection and localization compared to other target detection models. This demonstrates that LE-YOLO is more competitive in apple detection and localization under adverse light conditions. Compared to traditional manual and general automated harvesting, our method enables automated work under various adverse light conditions, significantly improving harvesting efficiency, reducing labor costs, and providing a feasible solution for automation in the field of apple harvesting.

Профилирование потребителей как инструмент совершенствования управления дебиторской задолженностью энергосбытовой организации

The problem facing an energy sales company is the collection of overdue accounts receivable (RA). The valuable achievements that energy sales companies have achieved while working to debtors are constantly being improved. So, specialists in working with remote control of an energy sales company need to make attempts to resolve the situation and search for compromises. It can be concluded to reduce the costs associated with work on managing debt, it is necessary to segment consumers according to the degree of possibility of debt collection and develop tools for interacting with consumers. It is proposed to partially solve the problems of accounts receivable by introducing an energy resource consumer profile. The research base is 703,726 telephone calls from operators with debtors of an energy sales organization. We grouped subscribers into 4 synonymous directions after we studied the subscriber's answers: Forgetful, Aggressive, With a special social status, Not available. We used the sorting method, the comparison method and the logical method. The operator’s work must be unified in order to sort subscribers according to the above principle at minimal cost. A standardized interview was developed for this purpose. The effectiveness of the proposed measures was manifested in the reducing labor costs and the increasing operator productivity. The importance of using a questionnaire survey allows employees to direct free energy and time to other operational tasks.

Automated system for intertidal mollusk keeping with tidal cycle imitation

The article describes a working prototype of an automated laboratory setup for intertidal mollusk keeping with tidal cycle imitation. The setup currently consists of two groups of five aquariums. At high tide, water is gravity fed into the aquariums from a common water tank; at low tide, water is gravity drained into a common collection tank. Water is transferred between the tanks using a special pump. The control unit consists of a networked Arduino Mega board and two Arduino Nano boards. The Arduino Mega board is responsible for the environmental monitoring systems, data logging, and automatic seawater change, as well as a software-based selfchecking and decision-making system. One of the Arduino Nano boards is responsible for realizing the tidal cycle; the other board is responsible for the remote monitoring and control of the setup via SMS. The results of pilot experiments indicate a significant reduction in labor costs in mollusk keeping. The developed setup can be used to keep a wide range of intertidal animals for studying their biology, assessing the impact of environmental factors, and biotesting, as well as for educational purposes.

An efficient method for detecting Camellia oleifera fruit under complex orchard environment

Efficient Camellia oleifera fruit detection in complex orchard environments is one of the most crucial technologies for automated picking and is significant for reducing labor costs. At present, Camellia oleifera fruit detection remains challenging due to the influence of fruit trait differences, cultivar differences, orchard environment differences, complex illumination, and occlusion. To address these challenges, a modified YOLOv7 model SDF-YOLO (SKConv-Decoupled Head-Focal EIoU-YOLOv7) has been developed. In SDF-YOLO, the selective kernel convolution (SKConv) is introduced to detect fruits of different sizes resulting from trait difference and occlusion with high performance, which utilizes multiple branches with different kernel sizes to fully extract and fuse the multi-scale information. To fully extract feature information of occlusion fruits, the decoupled head is used as the head network of SDF-YOLO to enhance the generalization performance of SDF-YOLO to cultivar differences and orchard environment differences. In addition, focal efficient intersection over union (Focal EIoU) loss is introduced to focus on high-quality bounding boxes, which can deal with positive and negative sample imbalance problems caused by occlusion and complex illumination. Experiment results demonstrate that SDF-YOLO can effectively detect Camellia oleifera fruits in complex orchard environments. Compared with eight mainstream models shows that SDF-YOLO achieves the highest precision of 94.91 %, recall of 93.01 %, F1 score of 93.95 %, and mAP of 96.65 % with an encouraging mean detection time of 36.57 ms. Moreover, SDF-YOLO exhibits robust adaptability to fruit trait differences, cultivar differences, orchard environment differences, complex illumination, and occlusion. These results indicated that SDF-YOLO is feasible for Camellia oleifera fruit detection in complex orchard environments, which provided a technical foundation for the automated picking of commercial orchards.

Deep Reinforcement Learning and Auto-Differential Evolution Co-Guided Coal Washing

Background. Coal washing is a complicated process and difficult to control, which has many controlling parameters with strong coupling relationship. It is still a challenge to realize the self-perception, self-adjustment, and self-evaluation of coal washing machine, improve the quality of coal washing, ensure production safety, and reduce labor cost. Methods. Through the intelligent transformation of jig, this paper proposes an intelligent washing method with cooperated deep reinforcement learning and evolutionary computation. First, it designs a fault warning method based on statistical analysis, helping to recover the normal running state of jig with manual maintenance. Then, it constructs a regulation strategy generation method with deep reinforcement learning supported by the fusion of artificial experience and historical data. Last, for the lack of monitoring data caused by poor communication quality and environment, the regulation strategy prediction method with evolutionary computation and surrogate model is proposed. Results. In practice, this method shows accurate fault warning accuracy and rapid cleaned coal ash adjustment response ability. Conclusions. This shows that the method proposed in this paper is of great significance for intelligent washing and can better cope with the special situation when the washing equipment sensing data are missing.

Adaptive attention fusion network for cross-device GUI element re-identification in crowdsourced testing

The rapid growth of mobile devices has ushered in an era of different device platforms. Different devices require a consistent user experience, especially with similar graphical user interfaces (GUIs). However, the different code bases of the various operating systems as well as the different GUI layouts and resolutions of the various devices pose a challenge for automated software testing. Crowdsourced software testing (CST) has emerged as a viable solution where crowdsourced workers perform tests on their own devices and provide detailed bug reports. Although CST is cost-effective, it is not very efficient and requires a large number of workers for manual testing. The potential of optimizing CST reproduction testing through computer vision remains largely untapped, especially when considering the uniformity of GUI elements on different devices. In this study, we present a novel deep learning model specifically designed to re-identify GUI elements in CST reproduction test scenarios, regardless of the underlying code changes on different devices. The model features a robust backbone network for feature extraction, an innovative attention mechanism with learnable factors to enhance the features of GUI elements and minimize interference from their backgrounds, and a classifier to determine matching labels for these elements. Our approach was validated on a large GUI element dataset containing 31,098 element images for training, 115,704 element images from real apps for testing, and 67 different background images. The results of our experiments underline the excellent accuracy of the model and the importance of each component. This work is a major step forward in improving the efficiency of reproduction testing in CST. The innovative solutions we propose could further reduce labor costs for CST platforms.

AUTOMATION OF THE PROCESS AND OPTIMIZATION OF TECHNICAL DIAGNOSTICS RESULTS PROCESSING

High safety requirements are imposed on the operation of production facilities. One of the requirements is periodic diagnostics of equipment and structures. Technical diagnostics is the recognition of the state of a technical system, which includes a wide rangeof problems associated with obtaining and evaluating diagnostic information. Surveys provide extensive information about the diagnostic object, which allows us to conclude about the operability of the object, to make a decision about repairs or modernization.The article discusses the process of diagnostics, its shortcomings and methods of their elimination. A program for automating the process and processing the results of technical diagnostics is proposed. The implementation of the development is aimed at increasing the reliability of the data obtained, reducing labor costs, and improving the state of safety at production facilities. It is also assumed that when using the development, the number of industrial accidents that occurred due to human factor errors during technical diagnostics will be reduced.The program consists of four elements, which will allow them to be gradually implemented in production: a sensor-server commu-nication system, a database, an electronic technological scheme of the object, a computing module. The communication system will simplify the transmission of data from the control sites. The database will facilitate work with documentation for control objects. The information available on the electronic technological scheme will increase the speed of interaction of employees of various services during diagnostics. The calculation module will eliminate the human factor during calculations and accelerate the formation of control acts and the conclusion of the industrial safety expertise.The joint use of all elements of the program will facilitate and accelerate the work on technical diagnostics and simplify the work with documentation for research objects.

Managing under austerity: a qualitative study of management–union relations during attempts to cut labour costs in three South African public hospitals

PurposeIn this paper, the authors examine the strategies used to reduce labour costs in three public hospitals in South Africa, which were effective and why. In the democratic era, after the revelations of large-scale corruption, the authors ask whether their case studies provide lessons for how public service institutions might re-make themselves, under circumstances of austerity.Design/methodology/approachA comparative qualitative case study approach, collecting data using a combination of interviews with managers, focus group discussions and interviews with shop stewards and staff was used.FindingsManagement in two hospitals relied on their financial power, divisions between unions and employees' loyalty. They lacked the insight to manage different actors, and their efforts to outsource services and draw on the Extended Public Works Program failed. They failed to support staff when working beyond their scope of practice, reducing employees' willingness to take on extra responsibilities. In the remaining hospital, while previous management had been removed due to protests by the unions, the new CEO provided stability and union–management relations were collaborative. Her legitimate power enabled unions and management to agree on appropriate cost cutting strategies.Originality/valueFinding an appropriate balance between the new reality of reduced financial resources and the needs of staff and patients, requires competent unions and management, transparency and trust to develop legitimate power; managing in an authoritarian manner, without legitimate power, reduces organisational capacity. Ensuring a fair and orderly process to replace ineffective management is key, while South Africa grows cohorts of competent managers and builds managerial experience.

Design and Development of Automated Control of Restaurant Robot

This paper presents a novel voice-controlled restaurant robot system designed to revolutionize the dining experience by seamlessly integrating advanced robotics and natural language processing technologies. The proposed system aims to enhance customer service, operational efficiency, and overall dining satisfaction by providing a user-friendly and interactive platform. .The restaurant robot is equipped with a comprehensive set of capabilities, including, voice recognition and manipulation. By leveraging voice control, users can interact with the robot using natural language commands, enabling seamless communication and intuitive control. This allows customers to effortlessly place orders The system's is comprises a mobile robotic platform, bluetooth modules, a cloud-based natural language processing, The voice control functionality is implemented through a sophisticated speech recognition system, enabling the robot to accurately understand and interpret user commands. .In addition to customer-facing interactions, the restaurant robot also assists restaurant staff in various tasks. Deliver food and beverages to tables, handle payment transactions, and perform routine cleaning tasks. By automating these processes, the system improves operational efficiency, reduces labor costs, and allows human staff to focus on highervalue activities such as customer engagement and culinary expertise. .To evaluate the performance and usability of the voice-controlled restaurant robot, a series of experiments were conducted in a real-world restaurant environment. The results demonstrated the system's high recognition accuracy, robustness in noisy environments, and its ability to effectively handle a wide range of user requests. .The proposed voice-controlled restaurant robot presents a promising solution for transforming the dining experience. It combines the convenience of voice control with advanced robotics, enabling seamless and interactive interactions between customers, staff, and the robotic platform. The system's ability to enhance customer service, operational efficiency, and overall dining satisfaction positions it as a significant advancement in the hospitality industry.

Jamming problems and the effects of compliance in dual peg-hole disassembly

Disassembly is a crucial step in remanufacturing and is currently mainly performed by humans. Automating disassembly can reduce labour costs and make remanufacturing more economically attractive. This paper focuses on identifying and characterizing a common disassembly task, dual peg-hole disassembly, with the aim of building a robotic disassembly system for this task. We enumerate the possible contact states and their geometric conditions during the extraction of two studs in a dual peg-hole. This paper focuses on jamming in the extraction and conducts geometrical and quasi-static analyses to determine the boundary conditions of jamming. Based on the analyses, this paper also investigates the role of active compliance as a solution to avoid jamming. We also simulate critical variables and examine key parameters such as the degree of compliance, the location of the compliance centre and initial position errors. Finally, we conduct experimental studies on dual peg-hole extraction with different compliance centres obtained using active compliance.

The Role and Potential of Intelligent Robots in The Garment Manufacturing Industry in The Industry 5.0 Era

The era of Industry 5.0 marks a new stage in the digital transformation of manufacturing, in which intelligent robotics play a key role. In the garment manufacturing industry, intelligent robots can improve production efficiency, reduce labor costs, optimize product quality, and meet the needs of personalized customization. This article explores the potential and role of intelligent robots in the garment manufacturing industry in the era of Industry 5.0, and analyzes the challenges they face and future development trends.

A Method for Matching Information of Substation Secondary Screen Cabinet Terminal Block Based on Artificial Intelligence

The matching of schematic diagrams and physical information of terminal blocks in substation secondary screen cabinets plays a crucial role in the operation and maintenance of substations. To enhance the automation level of this task and reduce labor costs, a method for identifying and matching information of terminal blocks in substation secondary screen cabinets based on artificial intelligence is investigated in this paper. Initially, multi-layer object detection networks, tailored to the characteristics of both the schematic diagrams and the physical entities in substation secondary screen cabinets, are designed for the precise extraction of information. Subsequently, network topologies for both the schematic and physical systems are established using the Neo4j database, which allows for the digital storage of information in the substation secondary screen cabinet systems. Finally, the branch-and-bound method, improved by the application of a multi-modular graph convolutional network (MGCN) and deep Q-network (DQN), is employed to solve the maximum common subgraph (MCS) problem, resulting in the rapid and efficient matching of schematic and physical data.